This document summarizes a presentation about innovating analytics with Oracle Data Mining and R. The presentation introduces data mining and R, how they can be used with Oracle BI 11g, and Oracle's predictive analytics stack. It provides examples of data mining use cases and encourages organizations to start predictive analytics projects by leveraging existing BI investments. The presentation aims to provide an understanding of data mining and R, how predictive analytics can benefit organizations, and how to get started with a predictive analytics project.

1. Innovate Analytics with Oracle Data Mining & R
Christian Screen, Capgemini
Oracle Analytics Practice (formerly BI Consulting Group)
Tuesday December 11, 2012
Better intelligence, smarter decisions

2. Christian Screen
o Solutions Engineer at Capgemini
o15 Years in Technology
o Co-Author of Oracle BIEE 11g – A Hands-On Tutorial
o Podcast & Blog at ArtofBi.com
o Oracle ACE
o Oracle Deputy CTO
o Oracle Hyperion Certified Consultant
o BI Evangelist
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

3. Agenda
o What Data Mining?
o What is R?
o Oracle BI 11g + Oracle Data Mining!
o Use Cases
o Getting your Organization Started
o Oracle Endeca & Data Mining
o With Oracle BI 11g
o Oracle Predictive Analytics stack - Tie it all together
oQ&A
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

4. Getting the Message Today
What I want to leave you with today…
 An understanding of Data Mining and R
 How Predictive Analytics is what your organization needs now
 Motivation for starting a Predictive Analytics project
 How Oracle BI 11g works with Data Mining & R
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

5. What is Data Mining?
“The process that attempts to discover
patterns and hidden knowledge in large data sets in order to
aid the decision making process.”
Discovery Patterns Clustering
Affinities
Large Data Sets Algorithms
Regression
Unbiased
Classification
Fraud Detection
Anomaly Detection Association Analysis
Probabilities
Predictive Analytics Summarization Statistical Confidence
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

6. Data Mining Use Cases
Example # 1 – Example #2 –
Market Basket Analysis Relationship Patterns
 Determine combination of items  Think Linked-In and Facebook
purchased together generating highest  Based on similar existing data points
or lowest margin locate other accounts and rank based
 Predict new item sales when sold on affinity
together with product sold at discount  Predict how may friends/people may
price accept an offer based on previous
 Calculate based on previous sales of behavior and the behavior of similar
individual products which store would users
benefit most from joined product sales
 Example: computer + monitor, printer
+ paper
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

7. Oracle Data Mining
Oracle’s DM Solution for Solving Business Problems
 Oracle Data Mining is installed automatically
when you install Oracle Database Enterprise
Edition.
 GUI is included with SQL Developer
 A mature DM Platform
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

8. What is R?
o A Open Source programming language and software environment
for statistical computing and graphics.
o Standard developed and maintained by the R Foundation
o Part of the Free Software Foundation GNU project
o Watch Movie: Revolution OS (2002)
o Client or Client Server
o Many images available on Amazon EC2
o R-Studio seems to be a leading Open Source IDE for R
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

9. What is R?
What Else?
o Really good at statistical and graphical techniques, including linear
and nonlinear modeling, classical statistical tests, time-series analysis,
classification, clustering, and others
o Text Mining and Lexical scoping
o Extends via user created Libraries/Packages
o Automated report and document generation
o Think associations, algorithms, statistics and
surprisingly good graphics
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

10. Data Mining vs. R
R Data Mining
 Does not require a database  Develop Statistical Models
 Complex algorithms can require large  Resides in the Database
amounts of memory and processor  No visual output
 Visual output built in or by libraries  SQL Developer plug-in available
 User-built libraries make for expansive  Leverage some existing DB skills
capabilities and logic to be developed  Likes cleansed data
 Skill-up required for R Script language
 Data does not need to be clean though
it helps
 Uses connectors for data sources
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

11. Oracle BI & Data Mining / R
Getting the Organization Started is Simple
 Start today! Think Competitive Advantage.
 Data Scientists are heroes
 Experimentation is key
 Leverage existing investments
 Its part of Business Intelligence
 Used by a few, developed by fewer
 Get Endeca Information Discovery
 Get R & R-Studio
 Explore Predictive Analytics
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

12. Oracle BI & Data Mining / R
Bringing it All Under Business Intelligence makes sense…
 Traditional BI is necessary but can become stagnant
 Predictive Analytics complements BI
 Existing BI investments distribute Predictive Analytics
 All areas of an organization can benefit
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

13. Oracle Predictive Analytics Now
BI, Discovery, Data Mining, & R
Traditional BI / Analytics Predictive Analytics
 Tells us how we’re doing  Includes traditional BI
today concepts
 Distributes information  Expands the BI Team
 Tracks Performance Metrics  Expands knowledge of
 Makes sense of ERP data information
 Tactically targets specific
scenarios of the business
 Provides non-linear
perspective on the business
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

14. Oracle Predictive Analytics
Endeca Information Discovery (EID) vs. Oracle Data Mining / R
 Endeca is mainly a three part tool (ETL, Server, Client)
• Data Mining typically depends on a cleansed source, i.e. DW
 Endeca includes powerful features, visualizations, and guided search
navigation, perfect for self-service discovery
• Data Mining is database driven so no inherent graphics
• R has visuals and client tools for development but is not distributed
 Endeca projects perform well in an iterative design-develop-feedback
loop
• Data Mining and R are built from models and algorithms are typically consumed
by other systems
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

15. Oracle Predictive Analytics Stack
Engineered to work together…
 Oracle Advanced Analytics in Oracle 11g R2 RDBMS
• Data Mining & Enterprise R
o Aims to eliminate need for SAS, SPSS, Matlab, etc.
 Enterprise-R
• Oracle’s flavor of R
Start Planning Now for
 Exadata Machine Running your business
 Exalytics (as consumer) in the future of Analytics.
• Endeca
• OBIEE
• Essbase
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

16. Upcoming Endeca Webinar
Endeca Information Discovery in the Wild
 Predictive Advertising Analytics in Media
 Scott Schlesinger Paper on Predictive Analytics in the Movies
(http://www.digitalmarketingsuite.com/url/60534)
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

17. Data Mining with Oracle BI 11g
Oracle R Enterprise in Oracle BI Dashboards
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

18. Data Mining with Oracle BI 11g
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

19. Oracle R with Oracle BI 11g
ORE in Oracle BI 11g dashboard – Flight Delays
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

20. Data Mining & R with Oracle BI 11g
Embedded R Script using Parameterization from Oracle BI
select *
from table(rqTableEval(
cursor(select
ARRDELAY,DISTANCE,DEPDELAY,YEAR,MONTH,DAYOFMO
NTH,DEPTIME,ARRTIME,UNIQUECARRIER,FLIGHTNUM,ORI
GIN,DEST, ORIGIN||'-'||DEST ROUTE
from ontime_s
where year >= valueof(NQ_SESSION.OR_ARG1) and
year <= valueof(NQ_SESSION.OR_ARG2)
and DEPDELAY is not NULL),
cursor(select 1 max1, 1 pos1, 'mod' name1,
to_number(null) max2, to_number(null) pos2,
to_char(null) name2, total, chunk, value
from ontime_lm),
'select ARRDELAY, DISTANCE,
DEPDELAY,YEAR,MONTH,DAYOFMONTH,DEPTIME,ARRTIM
E,UNIQUECARRIER,FLIGHTNUM,ORIGIN,DEST, ORIGIN||''-
''||DEST ROUTE, 1 PRED from ontime_s',
'PredictDelays-score'))
order by 1, 2, 3
| Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

21. Questions?
Better intelligence, smarter decisions
© 2012 Capgemini. All rights reserved.

22. Contact Christian Screen
christian.screen@capgemini.com
Access Oracle BI Training at
Capgemini’s BICG University
link
www.capgemini.com/bim
The information contained in this presentation is proprietary. ©2012 Capgemini. All rights reserved